How to use from
SGLang
Install from pip and serve model
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mstyslavity/5tiny" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mstyslavity/5tiny",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'
Use Docker images
docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mstyslavity/5tiny" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mstyslavity/5tiny",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'
Quick Links

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the Linear merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: Jiayi-Pan/Tiny-Vicuna-1B
    parameters:
      weight: 0.2
  - model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
    parameters:
      weight: 0.2
  - model: yihanwang617/tinyllama-sft-vicuna-random-100k
    parameters:
      weight: 0.2
  - model: NickyNicky/cognitivecomputations_TinyDolphin-2.8-1.1b
    parameters:
      weight: 0.2
  - model: QuixiAI/TinyDolphin-2.8.2-1.1b
    parameters:
      weight: 0.2
merge_method: linear
dtype: float32
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Model size
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Tensor type
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